<<<<<<< HEAD ======= >>>>>>> 057e73b6f4affd15f95ae1a9b09e57b39811debd Group 29 Presentation <<<<<<< HEAD ======= >>>>>>> 057e73b6f4affd15f95ae1a9b09e57b39811debd

Group 29 Presentation

s224965: Mille Grinder
s224989: Amalie Frøsig
s225059: Niels Elfenbein
s250441: Tomás Raskovsky
s251977: Frida De los Rios

Introduction

  • CD molecules are membrane proteins with diverse functions and distributions across immune cell types.

  • Their expression patterns help distinguish cell lineages and reveal functional relationships

Aim:

  • How do the expression of CD markers on lymphocyte subsets change during maturation?

  • How are fluorescence intensity, variability, and positivity (MedQb, CVQb, PEpos) related across CD markers in lymphocyte subsets?

DOI: 10.5772/intechopen.81568

Materials and Methods

  • Data from: Frontiers in Immunology, “B Cell Biology,” vol. 10, Oct. 23, 2019. doi: 10.3389/fimmu.2019.02434

  • Can be downloaded from their shiny app: http://bioinformin.cesnet.cz/CDmaps/

  • The data set contains:

    • 28664 observations of 9 variables

    • 115 unique CDs and 38 unique cell types

Sample of data_aug:

<<<<<<< HEAD 
# A tibble: 5 × 8
<<<<<<< HEAD
  tissue CD      lineage     cell_type hierarchy   CVQb   MedQb   PEpos
  <chr>  <chr>   <chr>       <chr>         <dbl>  <dbl>   <dbl>   <dbl>
1 thymus CD1a    Thymocytes  CD8SP1ap          4   92.2  6184.  100    
2 blood  CD59    B cells     Bnaive            3   86.9  1862.   38    
3 blood  CD30    CD8 T cells TCD8CM            4 1108.     19.9   0.968
4 tonsil CD19    B cells     CB                3  151.  11779.   41.9  
5 blood  CD51_61 CD8 T cells TCD8CM            4  220.    228.    4.25
======= tissue CD lineage cell_type hierarchy CVQb MedQb PEpos <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> 1 blood CD80 CD8 T cells TCD8RAdim 4 162. 116. 2.65 2 blood CD45 T cells T 2 23.4 116379. 100 3 blood CD33 B cells BnatEff 3 310. 54.9 0.875 4 thymus CD16 Thymocytes DP3p 4 884. 6 2.02 5 thymus CD23 Thymocytes CD4SP1am 4 155. 29.8 0.0133 ======= 
# A tibble: 5 × 9
  tissue CD    lineage     cell_type hierarchy  CVQb   MedQb count PEpos
  <chr>  <chr> <chr>       <chr>         <dbl> <dbl>   <dbl> <dbl> <dbl>
1 tonsil CD100 B cells     UnswtMem          3  107. 5493.    3098 13.4 
2 blood  CD9   B cells     B27high           3  229.    9.58   310 22.6 
3 blood  CD69  CD8 T cells TCD8RAdim         4  547.   10.3   7137  4.27
4 blood  CD85d CD4 T cells TCD4TEMRA         4  207.  789.      21 19   
5 thymus CD98  Thymocytes  CD4SP1ap          4  102. 2408.    3267 94.7
>>>>>>> 057e73b6f4affd15f95ae1a9b09e57b39811debd >>>>>>> origin/main

Analysis 1

  • MedQb:
    • Three distinct clusters
    • Thymocytes cluster with T-cells from blood
    • B-cells form a tonsil cluster and a blood cluster
  • PEpos:
    • Four distinct clusters
    • Thymocytes and blood T cells are seperated in two clusters
    • B-cells from blood and tonsil seperate in PC2, but very similar in PC1

Analysis 2

  • Wide variations in CD marker distribution

  • Tissue-related clusters are common

  • Some markers are universally expressed (e.g. CD45), with others are lineage-specific

Analysis 3

CVQb:

  • Blue lines show a negative correlation → higher CVQb = lower MedQb

  • Tonsil B cells: line is flat → almost no relationship

PEpos:

  • Pink lines show a positive correlation → higher PEpos = higher MedQb

Analysis 4

  • How do CD marker expression change during maturation of each lineage?

    • Applying a linear model with naive cell as reference

  • B cells had the most significant markers, showing stronger activation changes

Analysis 4

  • Tonsil B cells are more activated (CD69, CD80), while blood B cells mature gradually (CD11a, CD80)

  • CD4 and CD8 T cells follow a similar pattern. Develop in the thymus and become more specialized and mature in the blood. 

Analysis 5

  • CD4 and CD8 T cells go through parallel stages

  • How do the CD markers differ between CD4 and CD8 T cells for each stage?

    • SP1am and naive subsets have the lowest number of significant CDs → CD4 and CD8 more similar in CD expression in these stages

Analysis 5

  • As expected CD4 and CD8 are significant different for all pairs

  • CD59 is significant for all stages except TEMRA with a higher log(MedQb) for CD4 T cells

Discussion

Why did we choose the linear model to assess significant difference for CDs?

  • Simple method to compare each subset to the naive cell

  • An ANOVA could for example also have been used for pairwise comparison

Problems with missing values in the wide-format data set for PCA

  • Number of experiments for each CD differed → summarized the experiments by the mean

  • Not every CD marker was measured across all cell types → replaced the missing value with the median for that specific CD

  • Limits the variation in the data set, but necessary to avoid dropping observations